A new framework for modeling the bidirectional interplay between brain oscillations and cardiac sympathovagal activity

The study of functional brain-heart interplay (BHI) aims to describe the dynamical interactions between central and peripheral autonomic nervous systems. Here, we introduce the Sympathovagal Synthetic Data Generation Model, which constitutes a new computational framework for the assessment of functional BHI. The model estimates the bidirectional interplay with novel quantifiers of cardiac sympathovagal activity gathered from Laguerre expansions of RR series (from the ECG), as an alternative to the classical spectral analysis. The main features of the model are time-varying coupling coefficients linking Electroencephalography (EEG) oscillations and cardiac sympathetic or parasympathetic activity, for either ascending or descending direction of the information flow. In this proof-of-concept study, functional BHI is quantified in the direction from-heart-to-brain, on data from 16 human volunteers undergoing a cold-pressor test. Results show that thermal stress induces heart-to-brain functional interplay originating from sympathetic and parasympathetic activities and sustaining EEG oscillations mainly in the
$\delta$
and
$\gamma$
bands. The proposed computational framework could provide a viable tool for the functional assessment of the causal interplay between cortical and cardiac sympathovagal dynamics.